This study addresses research priority areas of patient-centered outcomes and health care efficiency in the Agency for Healthcare Research and Quality (AHRQ). Operating room (OR) scheduling is critical for OR management in any hospital setting, and this project utilizes information systems and industrial engineering technologies to address a critical health service need. The perioperative (peri-op) process encompasses three stages: 1) preoperative (pre-op); 2) intraoperative (intra-op); and 3) postoperative (post-op). Solutions to improving OR performance in the intra-op stage can only have negative impact on other stages and impair the performance of the whole perioperative (peri-op) process. OR management is complicated, involving two flows (information and patient) and three phases (OR planning, scheduling, and adaptive control). Because of this complexity in OR management, the standard practice is to use simple rules in scheduling and key performance indicators to evaluate performance. Our recent studies in production scheduling theory identified inconsistencies between every two adjacent stages in the peri-op process, which explains why the currently applied simple rules and key performance indicators (KPIs) are not suitable for OR scheduling. Solutions to OR management should be efficient at the unit level and effective at the hospital level. To achieve efficiency and effectiveness in OR scheduling, we will establish a scheme to evaluate the performance of OR scheduling across the peri-op process from the perspectives of time and quality. Specific Aim 1 (effectiveness of OR scheduling at the hospital level) smoothes the information flow between units across the peri-op process and reconciles conflicting incentives of local management of different units by identifying the relationship among units from the quality perspective. Specific Aim 2 (efficiency at the unit level) smoothes the patient flow from the time perspective by simulation and revising our former heuristics for flow shop scheduling. This simulation model will enable University of Kentucky (UK) HealthCare to carry out OR scheduling for elective surgeries, emergencies and coordination among its three hospitals. Moreover, the application of multi- objective optimization to the findings while extending statistic process control (SPC) technologies in manufacturing and industrial engineering to healthcare systems.